I merged them.
On Thu, Mar 30, 2017 at 4:29 AM, Michael Lawrence <micha...@gene.com> wrote:
> Yea I will have to port the recent fixes.
>
> On Wed, Mar 29, 2017 at 11:32 PM, Hervé Pagès <hpa...@fredhutch.org> wrote:
>> On 03/27/2017 09:43 AM, Michael Lawrence wrote:
>>>
>>> I committed a fix into R trunk with a regression test.
>>
>>
>> Thanks Michael. Any chance you can port the fix to the 3.4 branch?
>>
>> H.
>>
>>>
>>> On Mon, Mar 27, 2017 at 8:41 AM, Michael Lawrence <micha...@gene.com>
>>> wrote:
>>>>
>>>> My bad guys, I'll fix when I get to work.
>>>>
>>>> On Mon, Mar 27, 2017 at 3:59 AM, Martin Morgan
>>>> <martin.mor...@roswellpark.org> wrote:
>>>>>
>>>>> On 03/22/2017 01:12 PM, Hervé Pagès wrote:
>>>>>>
>>>>>>
>>>>>> Hi Martin,
>>>>>>
>>>>>> On 03/22/2017 03:17 AM, Martin Maechler wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Andrzej Oleś <andrzej.o...@gmail.com>
>>>>>>>>>>>> on Wed, 22 Mar 2017 10:29:57 +0100 writes:
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> > Just for the record, on R-3.3.2 Herve's code fails with the
>>>>>>> following error:
>>>>>>> > Error in x[TRUE] <- new("A") :
>>>>>>> > incompatible types (from S4 to logical) in subassignment type
>>>>>>> fix
>>>>>>>
>>>>>>> yes, (of course).... and I would be interested in a small
>>>>>>> reproducible example which uses _valid_ code.
>>>>>>
>>>>>>
>>>>>>
>>>>>> Looks like before performing the subassignment itself, [<- first tries
>>>>>> to coerce the RHS to the "mode" of the LHS by calling as.vector() on
>>>>>> the
>>>>>> former. So if we define an as.vector S3 method for A objects:
>>>>>>
>>>>>> setClass("A", representation(stuff="numeric"))
>>>>>> as.vector.A <- function (x, mode="any") x@stuff
>>>>>> a <- new("A", stuff=c(3.5, 0.1))
>>>>>> x <- numeric(10)
>>>>>> x[3:4] <- a
>>>>>
>>>>>
>>>>>
>>>>> The relevant stack trace is
>>>>>
>>>>> * frame #0: 0x000000010dded77a
>>>>> libR.dylib`R_has_methods(op=<unavailable>)
>>>>> + 74 at objects.c:1415
>>>>> frame #1: 0x000000010ddaabf4
>>>>> libR.dylib`Rf_DispatchOrEval(call=0x00007fcea36f68a8,
>>>>> op=0x00007fcea201a178,
>>>>> generic=0x000000010df0a185, args=<unavailable>, rho=0x00007fcea2053318,
>>>>> ans=0x00007fff51f60c48, dropmissing=<unavailable>, argsevald=1) + 404 at
>>>>> eval.c:3150
>>>>> frame #2: 0x000000010de4e658 libR.dylib`SubassignTypeFix [inlined]
>>>>> dispatch_asvector(x=<unavailable>, call=0x00007fcea36f68a8,
>>>>> rho=0x00007fcea2053318) + 295 at subassign.c:283
>>>>>
>>>>>
>>>>> The segfault is at objects.c:1415
>>>>>
>>>>> offset = PRIMOFFSET(op);
>>>>> if(offset > curMaxOffset || prim_methods[offset] == NO_METHODS
>>>>> || prim_methods[offset] == SUPPRESSED)
>>>>>
>>>>> where offset is negative and prim_methods[offset] fails.
>>>>>
>>>>> (lldb) p *op
>>>>> (SEXPREC) $8 = {
>>>>> sxpinfo = (type = 0, obj = 0, named = 2, gp = 0, mark = 1, debug = 0,
>>>>> trace = 0, spare = 0, gcgen = 1, gccls = 0)
>>>>> attrib = 0x00007fcea201a178
>>>>> gengc_next_node = 0x00007fcea21874e8
>>>>> gengc_prev_node = 0x00007fcea2019ff0
>>>>> u = {
>>>>> primsxp = (offset = -1576951432)
>>>>> symsxp = {
>>>>>
>>>>>
>>>>> 'op' is assigned from subassign.c:287, op = R_Primitive("as.vector")
>>>>>
>>>>> static Rboolean dispatch_asvector(SEXP *x, SEXP call, SEXP rho) {
>>>>> static SEXP op = NULL;
>>>>> SEXP args;
>>>>> Rboolean ans;
>>>>> if (op == NULL)
>>>>> op = R_Primitive("as.vector");
>>>>> PROTECT(args = list2(*x, mkString("any")));
>>>>> ans = DispatchOrEval(call, op, "as.vector", args, rho, x, 0, 1);
>>>>> UNPROTECT(1);
>>>>> return ans;
>>>>> }
>>>>>
>>>>> But as.vector is not a primitive, so gets R_NilValue. This is passed to
>>>>> DispatchOrEval, and then to R_has_methods.
>>>>>
>>>>> It seems like dispatch_asvector() was introduced by
>>>>>
>>>>> $ svn log -c69747
>>>>> ------------------------------------------------------------------------
>>>>> r69747 | lawrence | 2015-12-09 09:04:56 -0500 (Wed, 09 Dec 2015) | 3
>>>>> lines
>>>>>
>>>>> subassignment of an S4 value into an atomic vector coerces the value
>>>>> with as.vector
>>>>>
>>>>> ------------------------------------------------------------------------
>>>>>
>>>>> So maybe Michael can tell us about his thinking here.
>>>>>
>>>>> Also, should R_has_methods be robust to R_NilValue? And R_NilValue
>>>>> explicitly zero it's data?
>>>>>
>>>>> Martin
>>>>>
>>>>>
>>>>>
>>>>>>
>>>>>> then the code is now valid and we still get the segfault on Mac.
>>>>>>
>>>>>> I didn't define as.vector.A in my original minimalist reproducible
>>>>>> code in order to keep it as simple as possible.
>>>>>>
>>>>>> H.
>>>>>>
>>>>>>
>>>>>>> We have seen such examples with something (more complicated
>>>>>>> than, but basically like)
>>>>>>>
>>>>>>> df <- data.frame(x=1:5, y=5:1, m=matrix(-pi*1:30, 5,6))
>>>>>>> M <- Matrix::Matrix(exp(0:3),2)
>>>>>>> df[1:2,1:2] <- M
>>>>>>>
>>>>>>> which actually calls `[<-`, and then `[<-.data.frame` and
>>>>>>> always works for me but does seg.fault (in the CRAN checks of
>>>>>>> package FastImputation (on 3 of the dozen platforms,
>>>>>>>
>>>>>>>
>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__cran.r-2Dproject.org_web_checks_check-5Fresults-5FFastImputation.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=zUahQYlBHRwNf6lPnSA1515Rm-iL5ffQI7hUcDW-JkE&e=
>>>>>>>
>>>>>>>
>>>>>>> one of them is
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.r-2Dproject.org_nosvn_R.check_r-2Ddevel-2Dmacos-2Dx86-5F64-2Dclang_FastImputation-2D00check.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=Z7LkVlUzmdmhqxGNFl4LuMVxYwQQGHSV7KdpKCJu12k&e=
>>>>>>>
>>>>>>>
>>>>>>> I strongly suspect this is the same bug as yours, but for a case
>>>>>>> where the correct behavior is *not* giving an error.
>>>>>>>
>>>>>>> I have also written and shown Herve's example to the R-core team.
>>>>>>>
>>>>>>> Unfortunately, I have no platform where I can trigger the bug.
>>>>>>> Martin
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> > Cheers,
>>>>>>> > Andrzej
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> > On Wed, Mar 22, 2017 at 1:28 AM, Martin Morgan <
>>>>>>> > martin.mor...@roswellpark.org> wrote:
>>>>>>>
>>>>>>> >> On 03/21/2017 08:21 PM, Hervé Pagès wrote:
>>>>>>> >>
>>>>>>> >>> Hi Leonardo,
>>>>>>> >>>
>>>>>>> >>> Thanks for hunting down and isolating that bug! I tried to
>>>>>>> simplify
>>>>>>> >>> your code even more and was able to get a segfault with just:
>>>>>>> >>>
>>>>>>> >>> setClass("A", representation(stuff="numeric"))
>>>>>>> >>> x <- logical(10)
>>>>>>> >>> x[TRUE] <- new("A")
>>>>>>> >>>
>>>>>>> >>> I get the segfault about 50% of the time on a fresh R session
>>>>>>> on Mac.
>>>>>>> >>> I tried this with R 3.3.3 on Mavericks, and with R devel
>>>>>>> (r72372)
>>>>>>> >>> on El Capitan. I get the segfault on both.
>>>>>>> >>>
>>>>>>> >>> So it looks like a bug in the `[<-` primitive to me
>>>>>>> (subassignment).
>>>>>>> >>>
>>>>>>> >>
>>>>>>> >> Any insight from
>>>>>>> >>
>>>>>>> >> R -d valgrind -f herve.R
>>>>>>> >>
>>>>>>> >> where herve.R contains the code above?
>>>>>>> >>
>>>>>>> >> Martin
>>>>>>> >>
>>>>>>> >>
>>>>>>> >>
>>>>>>> >>> Cheers,
>>>>>>> >>> H.
>>>>>>> >>>
>>>>>>> >>> On 03/21/2017 03:06 PM, Leonardo Collado Torres wrote:
>>>>>>> >>>
>>>>>>> >>>> Hi bioc-devel,
>>>>>>> >>>>
>>>>>>> >>>> This is a story about a bug that took me a long time to
>>>>>>> trace. The
>>>>>>> >>>> behaviour was really weird, so I'm sharing the story in case
>>>>>>> this
>>>>>>> >>>> helps others in the future. I was originally writing it to
>>>>>>> request
>>>>>>> >>>> help, but then I was able to find the issue ^^. The story
>>>>>>> ends right
>>>>>>> >>>> now with code that will reproduce the problem with '$<-' from
>>>>>>> >>>> IRanges/S4Vectors.
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> During this Bioc cycle, frequently my package derfinder has
>>>>>>> failed to
>>>>>>> >>>> pass R CMD check in OSX. The error is always the same when it
>>>>>>> appears
>>>>>>> >>>> and sometimes it shows up in release, but not devel and
>>>>>>> viceversa.
>>>>>>> >>>> Right now (3/21/2017) it's visible in both
>>>>>>> >>>> https://urldefense.proofpoint.com/v2/url?u=http-3A__biocondu
>>>>>>> >>>> ctor.org_checkResults_release_bioc-2DLATEST_derfinder_
>>>>>>> >>>> morelia-2Dchecksrc.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfh
>>>>>>> >>>> Q&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=Bw-1Kqy-M_
>>>>>>> >>>> t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=RS-lsygPtDdgWKAhjA2BcSLk
>>>>>>> >>>> Vy9RxxshXWAJaBZa_Yc&e=
>>>>>>> >>>>
>>>>>>> >>>> and
>>>>>>> >>>> https://urldefense.proofpoint.com/v2/url?u=http-3A__biocondu
>>>>>>> >>>> ctor.org_checkResults_devel_bioc-2DLATEST_derfinder_toluca
>>>>>>> >>>> 2-2Dchecksrc.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3X
>>>>>>> >>>> eAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=Bw-1Kqy-M_
>>>>>>> >>>> t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=a_K-yK7w2LEV72lpHrpp0UoK
>>>>>>> >>>> Rru_7Aad74T5Uk0R-Fo&e=
>>>>>>> >>>> .
>>>>>>> >>>> The end of "test-all.Rout.fail" looks like this:
>>>>>>> >>>>
>>>>>>> >>>> Loading required package: foreach
>>>>>>> >>>> Loading required package: iterators
>>>>>>> >>>> Loading required package: locfit
>>>>>>> >>>> locfit 1.5-9.1 2013-03-22
>>>>>>> >>>> getSegments: segmenting
>>>>>>> >>>> getSegments: splitting
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying potential
>>>>>>> segments
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: segmenting information
>>>>>>> >>>> 2017-03-20 02:36:52 .getSegmentsRle: segmenting with
>>>>>>> cutoff(s)
>>>>>>> >>>> 16.3681899295041
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying candidate
>>>>>>> regions
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying region clusters
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying potential
>>>>>>> segments
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: segmenting information
>>>>>>> >>>> 2017-03-20 02:36:52 .getSegmentsRle: segmenting with
>>>>>>> cutoff(s)
>>>>>>> >>>> 19.7936614060235
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying candidate
>>>>>>> regions
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying region clusters
>>>>>>> >>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>>>>> >>>>
>>>>>>> >>>> *** caught segfault ***
>>>>>>> >>>> address 0x7f87d2f917e0, cause 'memory not mapped'
>>>>>>> >>>>
>>>>>>> >>>> Traceback:
>>>>>>> >>>> 1: (function (y, x, cluster, weights, smoothFun, ...) {
>>>>>>> >>>> hostPackage <- environmentName(environment(smoothFun))
>>>>>>> >>>> requireNamespace(hostPackage) smoothed <-
>>>>>>> .runFunFormal(smoothFun,
>>>>>>> >>>> y = y, x = x, cluster = cluster, weights = weights,
>>>>>>> ...) if
>>>>>>> >>>> (any(!smoothed$smoothed)) {
>>>>>>> smoothed$fitted[!smoothed$smoothed]
>>>>>>> >>>> <- y[!smoothed$smoothed] } res <- Rle(smoothed$fitted)
>>>>>>> >>>> return(res)})(dots[[1L]][[1L]], dots[[2L]][[1L]],
>>>>>>> dots[[3L]][[1L]],
>>>>>>> >>>> dots[[4L]][[1L]], smoothFun = function (y, x = NULL,
>>>>>>> cluster,
>>>>>>> >>>> weights = NULL, minNum = 7, bpSpan = 1000, minInSpan
>>>>>>> = 0,
>>>>>>> >>>> verbose = TRUE) { if (is.null(dim(y)))
>>>>>>> y <-
>>>>>>> >>>> matrix(y, ncol = 1) if (!is.null(weights) &&
>>>>>>> >>>> is.null(dim(weights))) weights <- matrix(weights,
>>>>>>> ncol =
>>>>>>> >>>> 1) if (is.null(x)) x <- seq(along =
>>>>>>> y) if
>>>>>>> >>>> (is.null(weights)) weights <- matrix(1, nrow =
>>>>>>> nrow(y),
>>>>>>> >>>> ncol = ncol(y)) Indexes <- split(seq(along = cluster),
>>>>>>> cluster)
>>>>>>> >>>> clusterL <- sapply(Indexes, length) smoothed <-
>>>>>>> >>>> rep(TRUE, nrow(y)) for (i in seq(along = Indexes)) {
>>>>>>> >>>> if (verbose) if (i%%10000 == 0)
>>>>>>> >>>> cat(".") Index <- Indexes[[i]] if
>>>>>>> (clusterL[i]
>>>>>>> >>>>
>>>>>>> >>>>> = minNum & sum(rowSums(is.na(y[Index, , drop
>>>>>>> =
>>>>>>> >>>>>
>>>>>>> >>>> FALSE])) == 0) >= minNum) { nn <-
>>>>>>> >>>> minInSpan/length(Index) for (j in 1:ncol(y)) {
>>>>>>> >>>> sdata <- data.frame(pos = x[Index], y = y[Index,
>>>>>>> >>>> j], weights = weights[Index, j]) fit <-
>>>>>>> >>>> locfit(y ˜ lp(pos, nn = nn, h = bpSpan),
>>>>>>> data =
>>>>>>> >>>> sdata, weights = weights, family = "gaussian",
>>>>>>> >>>> maxk = 10000) pp <- preplot(fit, where =
>>>>>>> "data", band
>>>>>>> >>>> = "local", newdata = data.frame(pos =
>>>>>>> x[Index]))
>>>>>>> >>>> y[Index, j] <- pp$trans(pp$fit) }
>>>>>>> >>>> } else { y[Index, ] <- NA
>>>>>>> >>>> smoothed[Index] <- FALSE } }
>>>>>>> >>>> return(list(fitted = y, smoothed = smoothed, smoother =
>>>>>>> "locfit"))
>>>>>>> >>>> }, verbose = TRUE, minNum = 1435)
>>>>>>> >>>> 2: .mapply(.FUN, dots, .MoreArgs)
>>>>>>> >>>> 3: FUN(...)
>>>>>>> >>>> 4: doTryCatch(return(expr), name, parentenv, handler)
>>>>>>> >>>> 5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
>>>>>>> >>>> 6: tryCatchList(expr, classes, parentenv, handlers)
>>>>>>> >>>> 7: tryCatch({ FUN(...)}, error = handle_error)
>>>>>>> >>>> 8: withCallingHandlers({ tryCatch({ FUN(...) },
>>>>>>> error =
>>>>>>> >>>> handle_error)}, warning = handle_warning)
>>>>>>> >>>> 9: FUN(X[[i]], ...)
>>>>>>> >>>> 10: lapply(X, FUN, ...)
>>>>>>> >>>> 11: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>>>> = ddd,
>>>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>>>> >>>> 12: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>>>> = ddd,
>>>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>>>> >>>> 13: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>>>> clusterChunks,
>>>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>>>> >>>> 14: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>>>> clusterChunks,
>>>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>>>> >>>> 15: .smootherFstats(fstats = fstats, position = position,
>>>>>>> weights =
>>>>>>> >>>> weights, smoothFunction = smoothFunction, ...)
>>>>>>> >>>> 16: findRegions(prep$position, genomeFstats, "chr21", verbose
>>>>>>> = TRUE,
>>>>>>> >>>> smooth = TRUE, minNum = 1435)
>>>>>>> >>>> 17: eval(exprs, env)
>>>>>>> >>>> 18: eval(exprs, env)
>>>>>>> >>>> 19: source_file(path, new.env(parent = env), chdir = TRUE)
>>>>>>> >>>> 20: force(code)
>>>>>>> >>>> 21: with_reporter(reporter = reporter, start_end_reporter =
>>>>>>> >>>> start_end_reporter, {
>>>>>>> lister$start_file(basename(path))
>>>>>>> >>>> source_file(path, new.env(parent = env), chdir = TRUE)
>>>>>>> >>>> end_context() })
>>>>>>> >>>> 22: FUN(X[[i]], ...)
>>>>>>> >>>> 23: lapply(paths, test_file, env = env, reporter =
>>>>>>> current_reporter,
>>>>>>> >>>> start_end_reporter = FALSE, load_helpers = FALSE)
>>>>>>> >>>> 24: force(code)
>>>>>>> >>>> 25: with_reporter(reporter = current_reporter, results <-
>>>>>>> >>>> lapply(paths, test_file, env = env, reporter =
>>>>>>> current_reporter,
>>>>>>> >>>> start_end_reporter = FALSE, load_helpers = FALSE))
>>>>>>> >>>> 26: test_files(paths, reporter = reporter, env = env, ...)
>>>>>>> >>>> 27: test_dir(test_path, reporter = reporter, env = env,
>>>>>>> filter =
>>>>>>> >>>> filter, ...)
>>>>>>> >>>> 28: with_top_env(env, { test_dir(test_path, reporter =
>>>>>>> reporter,
>>>>>>> >>>> env = env, filter = filter, ...)})
>>>>>>> >>>> 29: run_tests(package, test_path, filter, reporter, ...)
>>>>>>> >>>> 30: test_check("derfinder")
>>>>>>> >>>> An irrecoverable exception occurred. R is aborting now ...
>>>>>>> >>>>
>>>>>>> >>>> I was finally able to reproduce this error on my Mac OSX
>>>>>>> laptop after
>>>>>>> >>>> running R CMD build and R CMD check (same options as in Bioc)
>>>>>>> several
>>>>>>> >>>> times. It took me a while, but I figured out what's the exact
>>>>>>> code
>>>>>>> >>>> that's failing. It can be reproduced (noting that it won't
>>>>>>> always
>>>>>>> >>>> fail...) in OSX by running:
>>>>>>> >>>>
>>>>>>> >>>> library('derfinder')
>>>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>>>> >>>> chunksize=1e3,
>>>>>>> >>>> colsubset=NULL)
>>>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> Here is the output from my laptop one time it actually
>>>>>>> failed:
>>>>>>> >>>>
>>>>>>> >>>> library('derfinder')
>>>>>>> >>>>>
>>>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>>>> >>>> chunksize=1e3,
>>>>>>> >>>> colsubset=NULL)
>>>>>>> >>>>
>>>>>>> >>>>> prep <- preprocessCoverage(genomeData, cutoff=0,
>>>>>>> scalefac=32,
>>>>>>> >>>>> chunksize=1e3,
>>>>>>> >>>>>
>>>>>>> >>>> + colsubset=NULL)
>>>>>>> >>>>
>>>>>>> >>>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>>>> >>>>>
>>>>>>> >>>> 2017-03-21 16:37:39 findRegions: smoothing
>>>>>>> >>>>
>>>>>>> >>>> *** caught segfault ***
>>>>>>> >>>> address 0x7f958dbf2be0, cause 'memory not mapped'
>>>>>>> >>>>
>>>>>>> >>>> Traceback:
>>>>>>> >>>> 1: (function (y, x, cluster, weights, smoothFun, ...) {
>>>>>>> >>>> hostPackage <- environmentName(environment(smoothFun))
>>>>>>> >>>> requireNamespace(hostPackage) smoothed <-
>>>>>>> .runFunFormal(smoothFun,
>>>>>>> >>>> y = y, x = x, cluster = cluster, weights = weights,
>>>>>>> ...) if
>>>>>>> >>>> (any(!smoothed$smoothed)) {
>>>>>>> smoothed$fitted[!smoothed$smoothed]
>>>>>>> >>>> <- y[!smoothed$smoothed] } res <- Rle(smoothed$fitted)
>>>>>>> >>>> return(res)})(dots[[1L]][[1L]], dots[[2L]][[1L]],
>>>>>>> dots[[3L]][[1L]],
>>>>>>> >>>> dots[[4L]][[1L]], smoothFun = function (y, x = NULL,
>>>>>>> cluster,
>>>>>>> >>>> weights = NULL, minNum = 7, bpSpan = 1000, minInSpan
>>>>>>> = 0,
>>>>>>> >>>> verbose = TRUE) { if (is.null(dim(y)))
>>>>>>> y <-
>>>>>>> >>>> matrix(y, ncol = 1) if (!is.null(weights) &&
>>>>>>> >>>> is.null(dim(weights))) weights <- matrix(weights,
>>>>>>> ncol =
>>>>>>> >>>> 1) if (is.null(x)) x <- seq(along =
>>>>>>> y) if
>>>>>>> >>>> (is.null(weights)) weights <- matrix(1, nrow =
>>>>>>> nrow(y),
>>>>>>> >>>> ncol = ncol(y)) Indexes <- split(seq(along = cluster),
>>>>>>> cluster)
>>>>>>> >>>> clusterL <- sapply(Indexes, length) smoothed <-
>>>>>>> >>>> rep(TRUE, nrow(y)) for (i in seq(along = Indexes)) {
>>>>>>> >>>> if (verbose) if (i%%10000 == 0)
>>>>>>> >>>> cat(".") Index <- Indexes[[i]] if
>>>>>>> (clusterL[i]
>>>>>>> >>>>
>>>>>>> >>>>> = minNum & sum(rowSums(is.na(y[Index, , drop
>>>>>>> =
>>>>>>> >>>>>
>>>>>>> >>>> FALSE])) == 0) >= minNum) { nn <-
>>>>>>> >>>> minInSpan/length(Index) for (j in 1:ncol(y)) {
>>>>>>> >>>> sdata <- data.frame(pos = x[Index], y = y[Index,
>>>>>>> >>>> j], weights = weights[Index, j]) fit <-
>>>>>>> >>>> locfit(y ~ lp(pos, nn = nn, h = bpSpan),
>>>>>>> data =
>>>>>>> >>>> sdata, weights = weights, family = "gaussian",
>>>>>>> >>>> maxk = 10000) pp <- preplot(fit, where =
>>>>>>> "data", band
>>>>>>> >>>> = "local", newdata = data.frame(pos =
>>>>>>> x[Index]))
>>>>>>> >>>> y[Index, j] <- pp$trans(pp$fit) }
>>>>>>> >>>> } else { y[Index, ] <- NA
>>>>>>> >>>> smoothed[Index] <- FALSE } }
>>>>>>> >>>> return(list(fitted = y, smoothed = smoothed, smoother =
>>>>>>> "locfit"))
>>>>>>> >>>> }, verbose = TRUE, minNum = 1435)
>>>>>>> >>>> 2: .mapply(.FUN, dots, .MoreArgs)
>>>>>>> >>>> 3: FUN(...)
>>>>>>> >>>> 4: doTryCatch(return(expr), name, parentenv, handler)
>>>>>>> >>>> 5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
>>>>>>> >>>> 6: tryCatchList(expr, classes, parentenv, handlers)
>>>>>>> >>>> 7: tryCatch({ FUN(...)}, error = handle_error)
>>>>>>> >>>> 8: withCallingHandlers({ tryCatch({ FUN(...) },
>>>>>>> error =
>>>>>>> >>>> handle_error)}, warning = handle_warning)
>>>>>>> >>>> 9: FUN(X[[i]], ...)
>>>>>>> >>>> 10: lapply(X, FUN, ...)
>>>>>>> >>>> 11: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>>>> = ddd,
>>>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>>>> >>>> 12: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>>>> = ddd,
>>>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>>>> >>>> 13: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>>>> clusterChunks,
>>>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>>>> >>>> 14: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>>>> clusterChunks,
>>>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>>>> >>>> 15: .smootherFstats(fstats = fstats, position = position,
>>>>>>> weights =
>>>>>>> >>>> weights, smoothFunction = smoothFunction, ...)
>>>>>>> >>>> 16: findRegions(prep$position, genomeFstats, "chr21", verbose
>>>>>>> = TRUE,
>>>>>>> >>>> smooth = TRUE, minNum = 1435)
>>>>>>> >>>>
>>>>>>> >>>> Possible actions:
>>>>>>> >>>> 1: abort (with core dump, if enabled)
>>>>>>> >>>> 2: normal R exit
>>>>>>> >>>> 3: exit R without saving workspace
>>>>>>> >>>> 4: exit R saving workspace
>>>>>>> >>>>
>>>>>>> >>>> The traceback information ends at's
>>>>>>> bumphunter::loessByCluster().
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> I have successfully used the following code other times (see
>>>>>>> below)
>>>>>>> >>>> where I test the culprit line 100 times. By successfully, I
>>>>>>> mean that
>>>>>>> >>>> the code ran without problems... so it was unsuccessful at
>>>>>>> reproducing
>>>>>>> >>>> the problem.
>>>>>>> >>>>
>>>>>>> >>>> library('derfinder')
>>>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>>>> >>>> chunksize=1e3,
>>>>>>> >>>> colsubset=NULL)
>>>>>>> >>>>
>>>>>>> >>>> for(i in 1:100) {
>>>>>>> >>>> print(i)
>>>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>>>> >>>> }
>>>>>>> >>>> options(width = 120)
>>>>>>> >>>> devtools::session_info()
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> I had several R processes open the one time it did fail, but
>>>>>>> well,
>>>>>>> >>>> I've had multiple of them open the times that the code didn't
>>>>>>> fail. So
>>>>>>> >>>> having multiple R processes doesn't seem to be an issue.
>>>>>>> >>>>
>>>>>>> >>>> The line that triggers the segfault is used simply to test
>>>>>>> that
>>>>>>> >>>> passing the argument 'minNum' to bumphunter::loessByCluster()
>>>>>>> via
>>>>>>> >>>> '...' works. It's not a relevant test for derfinder and I was
>>>>>>> tempted
>>>>>>> >>>> to remove it, although before tracing the bug I talked with
>>>>>>> Valerie
>>>>>>> >>>> about not removing it. With the upcoming Bioconductor release
>>>>>>> I
>>>>>>> >>>> decided to finally trace the line that triggers the segfault.
>>>>>>> At this
>>>>>>> >>>> point I was feeling lost...
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> Running the following code seems to trigger the segfault more
>>>>>>> often (I
>>>>>>> >>>> got it like 4 times in a row):
>>>>>>> >>>>
>>>>>>> >>>> library('derfinder')
>>>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>>>> >>>> chunksize=1e3,
>>>>>>> >>>> colsubset=NULL)
>>>>>>> >>>> regs_s1 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE)
>>>>>>> >>>> regs_s2 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE, smoothFunction =
>>>>>>> >>>> bumphunter::runmedByCluster)
>>>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>>>> >>>>
>>>>>>> >>>> But then I can still run the same code without problems on a
>>>>>>> for loop
>>>>>>> >>>> for 100 times:
>>>>>>> >>>>
>>>>>>> >>>> library('derfinder')
>>>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>>>> >>>> chunksize=1e3,
>>>>>>> >>>> colsubset=NULL)
>>>>>>> >>>>
>>>>>>> >>>> for(i in 1:100) {
>>>>>>> >>>> print(i)
>>>>>>> >>>> regs_s1 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE)
>>>>>>> >>>> regs_s2 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE, smoothFunction =
>>>>>>> >>>> bumphunter::runmedByCluster)
>>>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>>>> >>>> }
>>>>>>> >>>> options(width = 120)
>>>>>>> >>>> devtools::session_info()
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> I next thought of going through findRegions() to produce
>>>>>>> simple
>>>>>>> >>>> objects that could reproduce the error. I had in mine sharing
>>>>>>> these
>>>>>>> >>>> objects so it would be easier for others to help me figure
>>>>>>> out what
>>>>>>> >>>> was failing. It turns out that this code segfaulted reliably
>>>>>>> (all the
>>>>>>> >>>> times I tested it at least):
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> library('derfinder')
>>>>>>> >>>> library('BiocParallel')
>>>>>>> >>>> library('IRanges')
>>>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>>>> >>>> chunksize=1e3,
>>>>>>> >>>> colsubset=NULL)
>>>>>>> >>>> fstats <- genomeFstats
>>>>>>> >>>> position <- prep$position
>>>>>>> >>>> weights <- NULL
>>>>>>> >>>> cluster <- derfinder:::.clusterMakerRle(position, 300L)
>>>>>>> >>>> cluster
>>>>>>> >>>> BPPARAM <- SerialParam()
>>>>>>> >>>> iChunks <- rep(1, length(cluster))
>>>>>>> >>>>
>>>>>>> >>>> fstatsChunks <- split(fstats, iChunks)
>>>>>>> >>>> posChunks <- split(which(position), iChunks)
>>>>>>> >>>> clusterChunks <- split(cluster, iChunks)
>>>>>>> >>>> weightChunks <- vector('list', length =
>>>>>>> length(unique(iChunks)))
>>>>>>> >>>>
>>>>>>> >>>> res <- bpmapply(bumphunter::loessByCluster, fstatsChunks,
>>>>>>> posChunks,
>>>>>>> >>>> clusterChunks, weightChunks, MoreArgs = list(minNum = 1435),
>>>>>>> >>>> BPPARAM = BPPARAM, SIMPLIFY = FALSE)
>>>>>>> >>>>
>>>>>>> >>>> y <- fstatsChunks[[1]]
>>>>>>> >>>> smoothed <- res[[1]]
>>>>>>> >>>>
>>>>>>> >>>> ## This segfaults:
>>>>>>> >>>> if(any(!smoothed$smoothed)) {
>>>>>>> >>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>>>> >>>> }
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> The objects on the line that fail are a list and an Rle:
>>>>>>> >>>>
>>>>>>> >>>> y
>>>>>>> >>>>>
>>>>>>> >>>> numeric-Rle of length 1434 with 358 runs
>>>>>>> >>>> Lengths: 1 5
>>>>>>> >>>> ... 1
>>>>>>> >>>> Values : 5.109484425367 3.85228949953674 ...
>>>>>>> >>>> 3.99765511645983
>>>>>>> >>>>
>>>>>>> >>>>> lapply(smoothed, head)
>>>>>>> >>>>>
>>>>>>> >>>> $fitted
>>>>>>> >>>> [,1]
>>>>>>> >>>> [1,] NA
>>>>>>> >>>> [2,] NA
>>>>>>> >>>> [3,] NA
>>>>>>> >>>> [4,] NA
>>>>>>> >>>> [5,] NA
>>>>>>> >>>> [6,] NA
>>>>>>> >>>>
>>>>>>> >>>> $smoothed
>>>>>>> >>>> [1] FALSE FALSE FALSE FALSE FALSE FALSE
>>>>>>> >>>>
>>>>>>> >>>> $smoother
>>>>>>> >>>> [1] "loess"
>>>>>>> >>>>
>>>>>>> >>>>> table(!smoothed$smoothed)
>>>>>>> >>>>>
>>>>>>> >>>>
>>>>>>> >>>> TRUE
>>>>>>> >>>> 1434
>>>>>>> >>>>
>>>>>>> >>>>> y[!smoothed$smoothed]
>>>>>>> >>>>>
>>>>>>> >>>> numeric-Rle of length 1434 with 358 runs
>>>>>>> >>>> Lengths: 1 5
>>>>>>> >>>> ... 1
>>>>>>> >>>> Values : 5.109484425367 3.85228949953674 ...
>>>>>>> >>>> 3.99765511645983
>>>>>>> >>>>
>>>>>>> >>>> So in my derfinder code I was assigning an Rle to a matrix,
>>>>>>> and that
>>>>>>> >>>> was the segfault. I have no idea why this doesn't always fail
>>>>>>> on OSX
>>>>>>> >>>> and why it never failed on Linux or Windows.
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> This is the super simplified IRanges code that fails:
>>>>>>> >>>>
>>>>>>> >>>> library('IRanges')
>>>>>>> >>>> y <- Rle(runif(10, 1, 1))
>>>>>>> >>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>>>> >>>> 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>>>> >>>> sessionInfo()
>>>>>>> >>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>>>> >>>>
>>>>>>> >>>> ## Segfault on OSX
>>>>>>> >>>>
>>>>>>> >>>> library('IRanges')
>>>>>>> >>>>> y <- Rle(runif(10, 1, 1))
>>>>>>> >>>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>>>> >>>>>
>>>>>>> >>>> + 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>>>> >>>>
>>>>>>> >>>>>
>>>>>>> >>>>> sessionInfo()
>>>>>>> >>>>>
>>>>>>> >>>> R Under development (unstable) (2016-10-26 r71594)
>>>>>>> >>>> Platform: x86_64-apple-darwin13.4.0 (64-bit)
>>>>>>> >>>> Running under: macOS Sierra 10.12.3
>>>>>>> >>>>
>>>>>>> >>>> locale:
>>>>>>> >>>> [1]
>>>>>>> en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>>>>>>> >>>>
>>>>>>> >>>> attached base packages:
>>>>>>> >>>> [1] stats4 parallel stats graphics grDevices utils
>>>>>>> >>>> datasets methods base
>>>>>>> >>>>
>>>>>>> >>>> other attached packages:
>>>>>>> >>>> [1] IRanges_2.9.19 S4Vectors_0.13.15
>>>>>>> BiocGenerics_0.21.3
>>>>>>> >>>>
>>>>>>> >>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>>>> >>>>>
>>>>>>> >>>>
>>>>>>> >>>> *** caught segfault ***
>>>>>>> >>>> address 0x7fcdc31dffe0, cause 'memory not mapped'
>>>>>>> >>>>
>>>>>>> >>>> Possible actions:
>>>>>>> >>>> 1: abort (with core dump, if enabled)
>>>>>>> >>>> 2: normal R exit
>>>>>>> >>>> 3: exit R without saving workspace
>>>>>>> >>>> 4: exit R saving workspace
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> ## No problems on Linux
>>>>>>> >>>>
>>>>>>> >>>> library('IRanges')
>>>>>>> >>>>> y <- Rle(runif(10, 1, 1))
>>>>>>> >>>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>>>> >>>>>
>>>>>>> >>>> + 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>>>> >>>>
>>>>>>> >>>>>
>>>>>>> >>>>> sessionInfo()
>>>>>>> >>>>>
>>>>>>> >>>> R version 3.3.1 Patched (2016-09-30 r71426)
>>>>>>> >>>> Platform: x86_64-pc-linux-gnu (64-bit)
>>>>>>> >>>> Running under: Red Hat Enterprise Linux Server release 6.6
>>>>>>> (Santiago)
>>>>>>> >>>>
>>>>>>> >>>> locale:
>>>>>>> >>>> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
>>>>>>> >>>> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
>>>>>>> >>>> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
>>>>>>> >>>> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
>>>>>>> >>>> [9] LC_ADDRESS=C LC_TELEPHONE=C
>>>>>>> >>>> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>>>>>>> >>>>
>>>>>>> >>>> attached base packages:
>>>>>>> >>>> [1] stats4 parallel stats graphics grDevices
>>>>>>> datasets utils
>>>>>>> >>>> [8] methods base
>>>>>>> >>>>
>>>>>>> >>>> other attached packages:
>>>>>>> >>>> [1] IRanges_2.8.2 S4Vectors_0.12.2
>>>>>>> BiocGenerics_0.20.0
>>>>>>> >>>> [4] colorout_1.1-2
>>>>>>> >>>>
>>>>>>> >>>> loaded via a namespace (and not attached):
>>>>>>> >>>> [1] tools_3.3.1
>>>>>>> >>>>
>>>>>>> >>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>>>> >>>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> Best,
>>>>>>> >>>> Leo
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> The session information for my first tests is below:
>>>>>>> >>>>
>>>>>>> >>>> devtools::session_info()
>>>>>>> >>>>>
>>>>>>> >>>> Session info
>>>>>>> >>>> ------------------------------------------------------------
>>>>>>> >>>> -----------------------------------------------
>>>>>>> >>>>
>>>>>>> >>>> setting value
>>>>>>> >>>> version R Under development (unstable) (2016-10-26 r71594)
>>>>>>> >>>> system x86_64, darwin13.4.0
>>>>>>> >>>> ui X11
>>>>>>> >>>> language (EN)
>>>>>>> >>>> collate en_US.UTF-8
>>>>>>> >>>> tz America/New_York
>>>>>>> >>>> date 2017-03-21
>>>>>>> >>>>
>>>>>>> >>>> Packages
>>>>>>> >>>> ------------------------------------------------------------
>>>>>>> >>>> ---------------------------------------------------
>>>>>>> >>>>
>>>>>>> >>>> package * version date source
>>>>>>> >>>> acepack 1.4.1 2016-10-29 CRAN (R 3.4.0)
>>>>>>> >>>> AnnotationDbi 1.37.4 2017-03-10 Bioconductor
>>>>>>> >>>> assertthat 0.1 2013-12-06 CRAN (R 3.4.0)
>>>>>>> >>>> backports 1.0.5 2017-01-18 CRAN (R 3.4.0)
>>>>>>> >>>> base64enc 0.1-3 2015-07-28 CRAN (R 3.4.0)
>>>>>>> >>>> Biobase 2.35.1 2017-02-23 Bioconductor
>>>>>>> >>>> BiocGenerics * 0.21.3 2017-01-12 Bioconductor
>>>>>>> >>>> BiocParallel 1.9.5 2017-01-24 Bioconductor
>>>>>>> >>>> biomaRt 2.31.4 2017-01-13 Bioconductor
>>>>>>> >>>> Biostrings 2.43.5 2017-03-19 cran (@2.43.5)
>>>>>>> >>>> bitops 1.0-6 2013-08-17 CRAN (R 3.4.0)
>>>>>>> >>>> BSgenome 1.43.7 2017-02-24 Bioconductor
>>>>>>> >>>> bumphunter * 1.15.0 2016-10-23 Bioconductor
>>>>>>> >>>> checkmate 1.8.2 2016-11-02 CRAN (R 3.4.0)
>>>>>>> >>>> cluster 2.0.6 2017-03-16 CRAN (R 3.4.0)
>>>>>>> >>>> codetools 0.2-15 2016-10-05 CRAN (R 3.4.0)
>>>>>>> >>>> colorout * 1.1-2 2016-11-15 Github
>>>>>>> >>>> (jalvesaq/colorout@6d84420)
>>>>>>> >>>> colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
>>>>>>> >>>> crayon 1.3.2 2016-06-28 CRAN (R 3.4.0)
>>>>>>> >>>> data.table 1.10.4 2017-02-01 CRAN (R 3.4.0)
>>>>>>> >>>> DBI 0.6 2017-03-09 CRAN (R 3.4.0)
>>>>>>> >>>> DelayedArray 0.1.7 2017-02-17 Bioconductor
>>>>>>> >>>> derfinder * 1.9.10 2017-03-17 cran (@1.9.10)
>>>>>>> >>>> derfinderHelper 1.9.4 2017-03-07 Bioconductor
>>>>>>> >>>> devtools 1.12.0 2016-12-05 CRAN (R 3.4.0)
>>>>>>> >>>> digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
>>>>>>> >>>> doRNG 1.6 2014-03-07 CRAN (R 3.4.0)
>>>>>>> >>>> foreach * 1.4.3 2015-10-13 CRAN (R 3.4.0)
>>>>>>> >>>> foreign 0.8-67 2016-09-13 CRAN (R 3.4.0)
>>>>>>> >>>> Formula 1.2-1 2015-04-07 CRAN (R 3.4.0)
>>>>>>> >>>> GenomeInfoDb * 1.11.9 2017-02-08 Bioconductor
>>>>>>> >>>> GenomeInfoDbData 0.99.0 2017-02-14 Bioconductor
>>>>>>> >>>> GenomicAlignments 1.11.12 2017-03-16 cran (@1.11.12)
>>>>>>> >>>> GenomicFeatures 1.27.10 2017-03-16 cran (@1.27.10)
>>>>>>> >>>> GenomicFiles 1.11.4 2017-03-10 Bioconductor
>>>>>>> >>>> GenomicRanges * 1.27.23 2017-02-25 Bioconductor
>>>>>>> >>>> ggplot2 2.2.1 2016-12-30 CRAN (R 3.4.0)
>>>>>>> >>>> gridExtra 2.2.1 2016-02-29 CRAN (R 3.4.0)
>>>>>>> >>>> gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
>>>>>>> >>>> Hmisc 4.0-2 2016-12-31 CRAN (R 3.4.0)
>>>>>>> >>>> htmlTable 1.9 2017-01-26 CRAN (R 3.4.0)
>>>>>>> >>>> htmltools 0.3.5 2016-03-21 CRAN (R 3.4.0)
>>>>>>> >>>> htmlwidgets 0.8 2016-11-09 CRAN (R 3.4.0)
>>>>>>> >>>> IRanges * 2.9.19 2017-03-15 cran (@2.9.19)
>>>>>>> >>>> iterators * 1.0.8 2015-10-13 CRAN (R 3.4.0)
>>>>>>> >>>> knitr 1.15.1 2016-11-22 CRAN (R 3.4.0)
>>>>>>> >>>> lattice 0.20-34 2016-09-06 CRAN (R 3.4.0)
>>>>>>> >>>> latticeExtra 0.6-28 2016-02-09 CRAN (R 3.4.0)
>>>>>>> >>>> lazyeval 0.2.0 2016-06-12 CRAN (R 3.4.0)
>>>>>>> >>>> locfit * 1.5-9.1 2013-04-20 CRAN (R 3.4.0)
>>>>>>> >>>> magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
>>>>>>> >>>> Matrix 1.2-8 2017-01-20 CRAN (R 3.4.0)
>>>>>>> >>>> matrixStats 0.51.0 2016-10-09 CRAN (R 3.4.0)
>>>>>>> >>>> memoise 1.0.0 2016-01-29 CRAN (R 3.4.0)
>>>>>>> >>>> munsell 0.4.3 2016-02-13 CRAN (R 3.4.0)
>>>>>>> >>>> nnet 7.3-12 2016-02-02 CRAN (R 3.4.0)
>>>>>>> >>>> pkgmaker 0.22 2014-05-14 CRAN (R 3.4.0)
>>>>>>> >>>> plyr 1.8.4 2016-06-08 CRAN (R 3.4.0)
>>>>>>> >>>> qvalue 2.7.0 2016-10-23 Bioconductor
>>>>>>> >>>> R6 2.2.0 2016-10-05 CRAN (R 3.4.0)
>>>>>>> >>>> RColorBrewer 1.1-2 2014-12-07 CRAN (R 3.4.0)
>>>>>>> >>>> Rcpp 0.12.10 2017-03-19 CRAN (R 3.4.0)
>>>>>>> >>>> RCurl 1.95-4.8 2016-03-01 CRAN (R 3.4.0)
>>>>>>> >>>> registry 0.3 2015-07-08 CRAN (R 3.4.0)
>>>>>>> >>>> reshape2 1.4.2 2016-10-22 CRAN (R 3.4.0)
>>>>>>> >>>> rngtools 1.2.4 2014-03-06 CRAN (R 3.4.0)
>>>>>>> >>>> rpart 4.1-10 2015-06-29 CRAN (R 3.4.0)
>>>>>>> >>>> Rsamtools 1.27.13 2017-03-14 cran (@1.27.13)
>>>>>>> >>>> RSQLite 1.1-2 2017-01-08 CRAN (R 3.4.0)
>>>>>>> >>>> rtracklayer 1.35.9 2017-03-19 cran (@1.35.9)
>>>>>>> >>>> S4Vectors * 0.13.15 2017-02-14 cran (@0.13.15)
>>>>>>> >>>> scales 0.4.1 2016-11-09 CRAN (R 3.4.0)
>>>>>>> >>>> stringi 1.1.2 2016-10-01 CRAN (R 3.4.0)
>>>>>>> >>>> stringr 1.2.0 2017-02-18 CRAN (R 3.4.0)
>>>>>>> >>>> SummarizedExperiment 1.5.7 2017-02-23 Bioconductor
>>>>>>> >>>> survival 2.41-2 2017-03-16 CRAN (R 3.4.0)
>>>>>>> >>>> testthat * 1.0.2 2016-04-23 CRAN (R 3.4.0)
>>>>>>> >>>> tibble 1.2 2016-08-26 CRAN (R 3.4.0)
>>>>>>> >>>> VariantAnnotation 1.21.17 2017-02-12 Bioconductor
>>>>>>> >>>> withr 1.0.2 2016-06-20 CRAN (R 3.4.0)
>>>>>>> >>>> XML 3.98-1.5 2016-11-10 CRAN (R 3.4.0)
>>>>>>> >>>> xtable 1.8-2 2016-02-05 CRAN (R 3.4.0)
>>>>>>> >>>> XVector 0.15.2 2017-02-02 Bioconductor
>>>>>>> >>>> zlibbioc 1.21.0 2016-10-23 Bioconductor
>>>>>>> >>>>
>>>>>>> >>>> _______________________________________________
>>>>>>> >>>> Bioc-devel@r-project.org mailing list
>>>>>>> >>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.et
>>>>>>> >>>> hz.ch_mailman_listinfo_bioc-2Ddevel&d=DwIGaQ&c=eRAMFD45gAfqt
>>>>>>> >>>> 84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=
>>>>>>> >>>> Bw-1Kqy-M_t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=hEBTd8bPfLVp6H
>>>>>>> >>>> oN3XSBk6ppmeRZhdLoB8VseYM_Byk&e=
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>
>>>>>>> >>
>>>>>>> >> This email message may contain legally privileged
>>>>>>> and/or...{{dropped:2}}
>>>>>>> >>
>>>>>>> >>
>>>>>>> >> _______________________________________________
>>>>>>> >> Bioc-devel@r-project.org mailing list
>>>>>>> >>
>>>>>>>
>>>>>>>
>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_bioc-2Ddevel&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=TAyV6oTRVnq_7U29cOp53zyNEu6sSL7iaaCRECw2YVs&e=
>>>>>>>
>>>>>>> >>
>>>>>>>
>>>>>>> > [[alternative HTML version deleted]]
>>>>>>>
>>>>>>> > _______________________________________________
>>>>>>> > Bioc-devel@r-project.org mailing list
>>>>>>> >
>>>>>>>
>>>>>>>
>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_bioc-2Ddevel&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=TAyV6oTRVnq_7U29cOp53zyNEu6sSL7iaaCRECw2YVs&e=
>>>>>>>
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> Bioc-devel@r-project.org mailing list
>>>>>>>
>>>>>>>
>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_bioc-2Ddevel&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=TAyV6oTRVnq_7U29cOp53zyNEu6sSL7iaaCRECw2YVs&e=
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> This email message may contain legally privileged and/or...{{dropped:2}}
>>>>>
>>>>> _______________________________________________
>>>>> Bioc-devel@r-project.org mailing list
>>>>>
>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_bioc-2Ddevel&d=DwIFaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=RPY3Djdcr6U0Wn55s72jyZEqDTHfRiT2ot-1pHjMBVQ&s=CtvTQ9rB8yHEYCbbLPsrRPopkPml1ZTkMplBhR0o_bI&e=
>>
>>
>> --
>> Hervé Pagès
>>
>> Program in Computational Biology
>> Division of Public Health Sciences
>> Fred Hutchinson Cancer Research Center
>> 1100 Fairview Ave. N, M1-B514
>> P.O. Box 19024
>> Seattle, WA 98109-1024
>>
>> E-mail: hpa...@fredhutch.org
>> Phone: (206) 667-5791
>> Fax: (206) 667-1319
>>
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